基于网格的混合神经网络计算平台研究与实现

为了模仿人脑的复杂功能,把各种相关类型的神经网络组织起来,形成一个大规模混合神经网络.根据此需求,使用自主研发的LabGrid技术开发了一个基于网格的混合神经网络计算平台,利用该平台设计了一种新的混合神经网络分类系统来对该平台进行测试.测试结果表明,该平台具有较高的效率和良好的容错性.与其它分类系统比较可知,该分类系统有较高的准确率,从而证明了模仿人脑建立大规模混合神经网络分类系统的可行性和有效性.     

基于模糊控制的驾驶疲劳检测

提出了一种判断疲劳程度的新方法,通过检测眼睛、嘴巴状态和头部位置等能够反应疲劳的生理特征,利用模糊控制器推理,得到人的疲劳状态的数值表示,改善了疲劳或者非疲劳的二值表示形式.通过肤色识别和阚值选取等方法得到人眼检测,进行边界提取的结果优于边缘检测.利用fisher线性分类器进行嘴唇和肤色的分类,提高了检测速度.模糊推理更精确的反应了人的疲劳程度,实验结果表明了检测方法的有效性和可信性.     

一种改进的AdaBoost算法在人脸检测系统中的应用

针对复杂背景条件下人脸检测的检测率低、速度慢的问题,提出了一种改进的AdaBoost算法,与遗传算法相结合,产生了一种识别率高、泛化能力好的强分类器,文中称之为GA-AdaBoost算法。该算法首先训练多个支持向量机作为弱分类器,然后用AdaBoost算法将多个弱分类器组合成一个强分类器,在组合的同时采用遗传算法对各弱分类器的权值进行全局寻优。最后,通过试验与传统AdaBoost进行对比,表明了该算法具有识别率高和速度快的优越性。     

Method of classifier selection using the genetic approach

Abstract: The paper presents a novel machine learning algorithm used for training a compound classifier system that consists of a set of area classifiers. Area classifiers recognize objects derived from the respective competence area. Splitting feature space into areas and selecting area classifiers are two key processes of the algorithm; both take place simultaneously in the course of an optimization process aimed at maximizing the system performance. An evolutionary algorithm is used to find the optimal solution. A number of experiments have been carried out to evaluate system performance. The results prove that the proposed method outperforms each elementary classifier as well as simple voting.     

基于改进联合模型的人脸表情识别

在联合主动表观模型和主动形状模型的基础上,充分挖掘标定点之间的联系,提出一种局部纹理模型构建方法。通过改进匹配算法提高特征点的定位精度和匹配速度。将该算法提取到的人脸表情特征输入最近邻分类器,分类结果表明其识别率较高。     

基于级联框架下的多特征融合人脸检测算法

针对AdaBoost人脸检测方法在高分辨率彩色图像上定位速度慢和误检率高的问题,提出一种多特征融合的人脸检测方法。该方法使用级联策略将多种特征分类器有效地组合起来,高效地利用各种特征之间的互补性,形成一种新型的高性能分类器。实验结果显示,该方法提高了检测速度、降低了误检率。     

基于后验概率制导的B-KNN文本分类方法

针对K最近邻(KNN)方法分类准确率高但分类效率较低的特点,提出基于后验概率制导的贝叶斯K最近邻(B-KNN)方法。利用测试文本的后验概率信息对训练集多路静态搜索树进行剪枝,在被压缩的候选类型空间内查找样本的K个最近邻,从而在保证分类准确率的同时提高KNN方法的效率。实验结果表明,与KNN相比,B-KNN的性能有较大提升,更适用于具有较深层次类型空间的文本分类应用。     

Corrective classification: Learning from data imperfections with aggressive and diverse classifier ensembling

Learning from imperfect (noisy) information sources is a challenging and reality issue for many data mining applications. Common practices include data quality enhancement by applying data preprocessing techniques or employing robust learning algorithms to avoid developing overly complicated structures that overfit the noise. The essential goal is to reduce noise impact and eventually enhance the learners built from noise-corrupted data. In this paper, we propose a novel corrective classification (C2) design, which incorporates data cleansing, error correction, Bootstrap sampling and classifier ensembling for effective learning from noisy data sources. C2 differs from existing classifier ensembling or robust learning algorithms in two aspects. On one hand, a set of diverse base learners of C2 constituting the ensemble are constructed via a Bootstrap sampling process; on the other hand, C2 further improves each base learner by unifying error detection, correction and data cleansing to reduce noise impact. Being corrective, the classifier ensemble is built from data preprocessed/corrected by the data cleansing and correcting modules. Experimental comparisons demonstrate that C2 is not only more accurate than the learner built from original noisy sources, but also more reliable than Bagging [4] or aggressive classifier ensemble (ACE) [56], which are two degenerated components/variants of C2. The comparisons also indicate that C2 is more stable than Boosting and DECORATE, which are two state-of-the-art ensembling methods. For real-world imperfect information sources (i.e. noisy training and/or test data), C2 is able to deliver more accurate and reliable prediction models than its other peers can offer.     

Detection and characterization of defects using GMR probes and artificial neural networks

This work presents an eddy-current testing system based on a giant magnetoresistive (GMR) sensing device. Non-destructive tests in aluminum plates are applied in order to extract information about possible defects: cracks, holes and other mechanical damages. Eddy-current testing (ECT) presents major benefits such as low cost, high checking speed, robustness and high sensitivity to large classes of defects. Coil based architecture probes or coil-magnetoresistive probes are usually used in ECT. In our application the GMR sensor is used to detect a magnetic field component parallel to a plate surface, when an excitation field perpendicular to the plate is imposed. A neural network processing architecture, including a multilayer perceptron and a competitive neural network, is used to classify defects using the output amplitude of the eddy-current probe (ECP) and its operation frequency. The crack detection, classification and estimation of the geometrical characteristics, for different classes of defects, are described in the paper.     

A distributed approach to enabling privacy-preserving model-based classifier training

This paper proposes a novel approach for privacy-preserving distributed model-based classifier training. Our approach is an important step towards supporting customizable privacy modeling and protection. It consists of three major steps. First, each data site independently learns a weak concept model (i.e., local classifier) for a given data pattern or concept by using its own training samples. An adaptive EM algorithm is proposed to select the model structure and estimate the model parameters simultaneously. The second step deals with combined classifier training by integrating the weak concept models that are shared from multiple data sites. To reduce the data transmission costs and the potential privacy breaches, only the weak concept models are sent to the central site and synthetic samples are directly generated from these shared weak concept models at the central site. Both the shared weak concept models and the synthetic samples are then incorporated to learn a reliable and complete global concept model. A computational approach is developed to automatically achieve a good trade off between the privacy disclosure risk, the sharing benefit and the data utility. The third step deals with validating the combined classifier by distributing the global concept model to all these data sites in the collaboration network while at the same time limiting the potential privacy breaches. Our approach has been validated through extensive experiments carried out on four UCI machine learning data sets and two image data sets.